Because the majority of adolescent health problems are amenable to behavioral intervention and most adolescents visit a healthcare provider once a year, hea lth behavior change interventions linked to clinic- based health information technologies hold significant promise for improving healthcare quality and subsequent behavioral health outcomes for adolescents. Recognizing the potential to leverage advances in machine learning and virtual narrative environments, the field of health behavior change is now well- positioned to design health behavior change systems that extend the reach of clinicians to realize significant impacts on behavior change for adolescent preventive health. With a focus on risky behaviors and an emphasis on alcohol use, the project has two specific aims: (1) design, develop, and iteratively refine a policy-based reinforcement learning behavior change system for preventive adolescent health, and (2) investigate the impact of a clinically integrated sample-efficient policy gradient-based behavior change system on adolescent behavior. The project w ill culminate w ith an investigation of the behavioral effects of the CHANGEGRADIENTS system using adolescent patients recruited from two outpatient primary care clinics within the UCSF Department of Pediatrics: Mt. Zion Pediatrics and the Adolescent/Young Adult Clinic. It is hypothesized that adolescents who interact with CHANGEGRADIENTS with reduce number of days of alcohol use, reduce binge drinking, and increase self-efficacy to engage in healthy behavior a nd avoid risky substance use. It is anticipated that CHANGEGRADIENTS will provide a testbed for a broad range of health behavior change research and serve as the foundation for next-generation personalized preventive healthcare through computationally-enabled behavior change that is designed to be tightly integrated into clinical practice workflow. By taking advantage of the high degree of adaptive interactivity offered by its personalized behavior change environment, CHANGEGRADIENTS holds significant potential for creating compelling interactions that promote self-efficacy and engagement in healthy lifestyle behaviors to prevent cancer through improving cancer-related behaviors and risk factors. REL EVAN CE (Se e in stru cti ons): The proposed research will develop and field test an innovative computationally-enabled personalized behavior change model that will provide a testbed for a broad range of health behavior change research and is designed to be integrated into clinical practice. With adaptive interactivity, CHANGEGRADIENTS holds significant potential for creating compelling interactions that promote self-efficacy and engagement in healthy lifestyle behaviors to prevent cancer through improving cancer-related behaviors and risk factors .